JP4155529B2 - X-ray CT system - Google Patents

Description

（２−２）偶数チャンネル
【数９】

【０１６４】
あるいは、偶数チャンネルのデータは、（２−１）で得た奇数チャンネルのデータを（１）のようにして補間しても良い。また、第１、第２高密度データの生成順序を逆にして、第２高密度データである高密度対向データを先に生成し、第１高密度データである高密度実データの生成を後にしてもよい。
【０１６５】
（３）ヘリカル補間処理
ヘリカル補間手段２９Ｒは、補間データ１記憶部２９Ｄおよび補間データ２記憶部２９Ｇよりそれぞれ第１高密度データ（実データ）および第２高密度データ（対向データ）を読み出して、スライス方向にヘリカル補間し、目的のスライス位置のデータを作成し補間データ３記憶部２９Ｉに記憶させる（ステップＳ２５）。補間は、第１実施形態例のように２点補間でも良いし、第４実施形態例のようにスライス方向のフィルタ処理でも良い。また、チャンネル方向やスライス方向のボケ回復処理（ Debluring処理）を施しても良い。
【０１６６】
（４）ファンビーム再構成処理
画像再構成部３１は、補間データ３記憶部２９Ｉに記憶された高密度データを使用して、通常のファンビーム再構成を行い、画像を得る（ステップＳ１９）。適用する再構成法は、第１実施形態例に使用したフィルタ補正逆投影法でも良いし、ファンパラ変換とフーリエ逆変換とを組み合わせて処理しても良い。
【０１６７】
図３０は、本第６実施形態例の変形例を示すフローチャート図である。第６実施形態例では、それぞれ生データから第１、第２高密度データ生成処理を行ったが、第１高密度データの生成結果を使用して第２高密度データを生成することもできる。
【０１６８】
（１）第１高密度データ生成処理（実データ）
高密度実データ生成手段２９Ｑは、実データ１と実データ２とを生データ記憶部２９Ｂから得て、それぞれの実データに対して、下式に従う同一ビューの隣接チャンネル間補間を行って、第１高密度データ群ｈｐ３を生成し、補間データ１記憶部２９Ｄに記憶させる（図３０のステップＳ３１）。補間の重みは全チャンネルで一定である。
【０１６９】
第ｊビューにおける第ｋチャンネルの実データをＤ（ｊ，ｋ）とすると、
【数１０】
ｈｐ３（ｊ，１） ＝Ｄ（ｊ，１）
ｈｐ３（ｊ，２×ｋ） ＝Ｄ（ｊ，ｋ）
ｈｐ３（ｊ，２×ｋ＋１）＝［Ｄ（ｊ，ｋ）＋Ｄ（ｊ，ｋ＋１）］／２
補間データ１は、第１，２，３，…，２０００チャンネルのデータとなる。
【０１７０】
（２）第２高密度データ生成処理（対向データ）
高密度対向データ生成手段２９Ｐは、補間データ１記憶部２９Ｄから必要なデータを読み出し、奇数チャンネルのデータに対しては、従来例に記載したＱＱ再構成のときの方法で（図３８参照）、該当する１チャンネル×２ビューのデータを補間し、偶数チャンネルのデータに対しては、従来例に記載した対向ビーム補間法で生成したデータと同様に、２ビューで収集した２チャンネルのデータを用いた４点補間により、それぞれ第２高密度データｈｐ４の生成を行い、補間データ２記憶部２９Ｇに記憶させる（図３０のステップＳ３３）。
【０１７１】
この補間式を以下に示す。
【０１７２】
【数１１】
１≦Ｋ≦２×Ｎch（＝２０００）について、
ｈｐ４（ｊ，Ｋ）＝ｈｐ３（ｊ＋Ｘ（Ｋ），Ｙ（Ｋ））
Ｙ（Ｋ）＝２×Ｎch−Ｋ＋１
Ｘ（Ｋ）＝{[( Ｋ−Ｃent ＣＨ) ×φ] ／［Ｎch×180]＋0.5}×Ｎview
Ｃent ＣＨ＝（２×Ｎch＋１）／２
こうして得られた第１、第２高密度データ、ｈｐ３，ｈｐ４を使用してヘリカル補間処理（図３０のステップＳ２５）及び画像再構成処理（図３０のステップＳ１９）を行うことは、第６実施形態例と同様であるので、以下の説明を省略する。
【０１７３】
以上説明した第６実施形態例およびその変形例は、第２、３、４、５の各実施形態例と任意に組み合わせることができる。すなわち、チャンネル方向の Debluring処理との組合せ、マルチスライスＣＴ装置への適用、スライス方向フィルタ処理との組合せ、マルチスライスＣＴ装置での高密度サンプリング・スキャン法によるヘリカルスキャンなどにも適用することができる。
【０１７４】
なお、本発明をマルチスライスＣＴ装置に適用する場合、実施形態例では検出器列数を４列として説明したが、４列に限らず２、３、５、６、７、８列等、任意の検出器列数を備えたマルチスライスＣＴ装置に適用できることは明らかである。
【０１７５】
また、画像再構成法についても、フィルタ補正逆投影法（コンボリューション法）に限らず、ファンパラ変換による逆投影演算や、高速フーリエ変換（ＦＦＴ）を用いた逆投影演算、フーリエ変換および逆フーリエを用いた画像再構成法、ライノグラムによる画像再構成等、各種の画像再構成法を利用できることは明らかである。
【０１７６】
さらに、実施形態例では、生データから２倍の密度を有する高密度データを生成したが、２倍に限らず、３倍、４倍等の密度を有する高密度データを生成して画像再構成することも本発明の範囲内である。例えば３倍密度の高密度データを生成する場合には、少数チャンネルの小数点以下の数を０．３３および０．６７とし、４倍密度の高密度データを生成する場合には、小数チャンネルの小数点以下の数を０．２５，０．５および０．７５とすれば良い。
【０１７７】
【発明の効果】
以上説明したように、請求項1記載の本発明によれば、検出手段が収集したデータのサンプリングピッチより細かいサンプリングピッチのデータ（細ピッチデータ）を生成して再構成するので、高い空間分解能のアキシャル面の画像を得ることができる。基本スライス厚の薄化とスライス方向のフィルタ処理により、さらにパーシャルボリューム効果が抑制され、アーチファクトの少ない高画質な画像が得られる。又、スライス方向のフィルタ処理で選択するフィルタを低分解能関数とすれば、スタック処理画像のような高画質な画像が得られる。
また請求項２記載の本発明によれば、検出手段が収集したデータのサンプリング点数より多いサンプリング点数のデータ（多点数データ）を生成して再構成するので、高い空間分解能のアキシャル面の画像を得ることができる。基本スライス厚の薄化とスライス方向のフィルタ処理により、さらにパーシャルボリューム効果が抑制され、しかもアーチファクトの少ない高画質な画像が得られる。又、スライス方向のフィルタ処理で選択するフィルタを低分解能関数とすれば、スタック処理画像のような高画質な画像が得られる。
【図面の簡単な説明】
【図１】本発明の各実施形態例を適用するＸ線ＣＴ装置のシステム構成図である。
【図２】同第１実施形態例における補間処理部のブロック図である。
【図３】同第１実施形態例における処理の概念図である。
【図４】同第１実施形態例における処理の流れを説明するフローチャート図である。
【図５】同第１実施形態例における処理として外挿補間を用いた場合の概念図である。
【図６】同第１実施形態例における処理として内挿補間を用いた場合の概念図である。
【図７】同第１実施形態例において画像再構成部の制御装置が補間処理を行う構成の場合のブロック図である。
【図８】同第２実施形態例における補間処理部のブロック図である。
【図９】チャンネル方向Debluring 処理の概念図である。
【図１０】４列マルチスライスＣＴ、Ｐitch＝４でヘリカルスキャンした場合の或る列の対向ビームを示す図である。
【図１１】右上図が４列マルチスライスＣＴ、Ｐitch＝４でヘリカルスキャンした場合の或る列の対向ビームを示す図である。
【図１２】同第３実施形態例の処理の概念図である。
【図１３】同第４実施形態例における補間処理部のブロック図である。
【図１４】同第４実施形態例における位相θのスキャンの状態と処理の概念図である。
【図１５】高密度サンプリングと組み合わせたフィルタ処理の概念図である。
【図１６】束ね処理あるいはエンハンス処理を行うフィルタ関数の形状の例を示す図である。
【図１７】Ｐitch＝２．５の高密度サンプリング・スキャン法における実データだけのスキャン図である。
【図１８】Ｐitch＝２．５の高密度サンプリング・スキャン法における実データと（中心チャンネルの）対向データのスキャン図である。
【図１９】複雑な順序で配列された複数のビューと複数の検出器列の実データを示す図である。
【図２０】４列マルチスライスＣＴにおけるＰitch＝３．５のスキャン図である。
【図２１】４列マルチスライスＣＴにおけるＰitch＝４．５のスキャン図である。
【図２２】基本スライス厚を半分にしてのＰitch＝４．５のスキャン図である。
【図２３】２列マルチスライスＣＴにおけるＰitch＝１．５のときのスキャン図である。
【図２４】２列マルチスライスＣＴにおけるピッチ１．５の場合の対向ビーム補間法を示す図である。
【図２５】４列マルチスライスＣＴにおけるピッチ３．５の場合の対向ビーム補間法を示す図である。
【図２６】４列マルチスライスＣＴにおけるピッチ４．５の場合の対向ビーム補間法を示す図である。
【図２７】本発明に係るＸ線ＣＴ装置の第６実施形態における補間処理部のブロック図である。
【図２８】本発明に係るＸ線ＣＴ装置の第６実施形態における処理の概念図である。
【図２９】同第６実施形態例における処理手順を示すフローチャート図である。
【図３０】同第６実施形態例の変形例における処理手順を示すフローチャート図である。
【図３１】シングルスライスＣＴの概略構成図である。
【図３２】コンベンショナルスキャンとヘリカルスキャンの概念図である。
【図３３】Ｘ線ＣＴ装置の画像再構成を説明する図である。
【図３４】マルチスライスＸ線ＣＴ装置のジオメトリを説明する図である。
【図３５】ＱＱオフセット取り付けの状態を説明する図である。
【図３６】ＱＱを説明する図である。
【図３７】ＱＱを説明する図である。
【図３８】ＱＱの概念を説明する図である。
【図３９】コンベンショナルスキャンとヘリカルスキャンにおけるスキャン図である。
【図４０】３６０°補間法を説明する図（Ａ）、対向ビーム補間法を説明する図（Ｂ）、対向ビームを説明する図（Ｃ）、対向ビームのサンプリング位置を説明する図 （Ｄ）である。
【図４１】或る第ｊビューのデータの対向ビーム補間の概念図である。
【図４２】３６０°補間法の概念図である。
【図４３】ヘリカルスキャンの対向ビーム補間を説明する図である。
【図４４】マルチスライスＣＴの概念図である。
【図４５】４列マルチスライスＣＴに３６０°補間法を適用した場合のスキャン図である。
【符号の説明】
１３…架台寝台制御部、１５…寝台移動部、２３…検出器、２７…データ収集部、２９…補間処理部、３１…画像再構成部。[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an X-ray CT apparatus, and more particularly to an X-ray CT apparatus that performs a helical scan.
[0002]
[Prior art]
A scanning method of a conventional X-ray CT apparatus (hereinafter abbreviated as CT) used for imaging a tomographic plane of a subject will be briefly described.
[0003]
(1) Fan beam (single slice) X-ray CT
As shown in FIG. 31, the current mainstream of CT is an X-ray focal point that generates a fan-shaped X-ray beam (fan beam) and a row of N-channel (for example, 1000 channels) detection elements in a fan shape or a straight line. Is a single-slice CT having detectors arranged in a row.
[0004]
The single slice CT collects X-ray intensity data (referred to as projection data) that has passed through the subject while rotating around the subject with the X-ray focal point and the detector as a pair. Projection data is collected Nview times, for example, 1000 times in one rotation, and image reconstruction is performed based on this data by a method described later.
One data collection is referred to as one view, data of one detection element or detection element group in one view is collectively referred to as one beam, and all beams in one view (data of all detection elements) are collectively referred to as actual data.
[0005]
(2) Two scan methods (conventional scan and helical scan)
Two typical CT scanning methods will be described.
The first scan method is a conventional scan shown in FIG.
[0006]
This method is a scanning method in which the periphery of a target cross section (for example, cross section A) is rotated once. To obtain images of a plurality of cross sections (for example, cross section A and cross section B), first, data is collected while rotating around the cross section A, and then a bed on which the subject is placed, or an X-ray focus and detector To bring the section B to the plane of rotation. Thereafter, data is collected while rotating around the section B in the same manner as the section A.
Therefore, in the conventional scanning method, when the imaging range is wide in the body axis direction (Z-axis direction) of the subject and when there are many target cross sections, the imaging time becomes long.
[0007]
The second scan method is a helical scan shown in FIG.
In this method, while the X-ray focal point and the detector are continuously rotated, the bed is moved in the body axis direction of the subject in synchronization with the rotation, and data is collected. The locus of the X-ray focus scans around the subject in a spiral. According to this scanning method, a wide range can be scanned at high speed.
Here, a coordinate system is defined as shown in FIG. The XY plane corresponds to the cross sections A and B scanned by the conventional scan, the Z-axis direction is the body axis direction of the subject, and the direction referred to as the slice direction in the single slice CT described above.
[0008]
(3) Normal image reconstruction of conventional scan
CT image reconstruction will be briefly described with reference to FIGS.
[0009]
In the case of a conventional scan, it consists of the following three steps. Here, as shown in FIG. 33 (A), a subject in which only the signal of the arrow at the center of rotation exists is assumed.
[0010]
[1] Data collection and correction
Collect data with conventional scans. Although only a part of the rotation angle is illustrated, it is usually 360 °, 180 ° + fan angle, or the like. The projection data is as shown in FIG. The projection data is corrected in consideration of various factors such as the sensitivity of the detector and the X-ray intensity to obtain raw data.
[0011]
[2] Convolution operation with reconstruction function
Convolve the raw data for each angle and the reconstruction function. The convolution data is shown in FIG. C ) And the surrounding area of the originally existing signal is depressed.
[0012]
[3] Back projection operation
The convolution data is added to all the pixels (pixels) on the X-ray passing path when the data is collected. FIG. D ) Indicates a back projection operation at a certain angle. If this is repeated for the required angle, only the original signal remains.
The processes [2] and [3] described above are combined and called a filter-corrected back projection method (convolution back projection method).
[0013]
(4) Conventional scan high resolution image reconstruction (QQ)
For example, a so-called QQ (Quarter-Quarter) process for improving the spatial resolution from 0.50 mm to 0.35 mm will be described.
[0014]
FIG. 34A is an observation of the axial plane (XY plane) from the Z-axis direction. When the effective field diameter FOV (Field of View) is 500 mm and the number of detector channels is 1000, the spatial resolution of the axial image obtained by the conventional image reconstruction of the conventional scan of (3) is about 0.50 mm. It is. FCD (Focus-Center-Distance, distance between X-ray focus and rotation center) and FDD (Focus-Detector-Distance, distance between X-ray focus and detector).
[0015]
As described above, while the normal spatial resolution is about 0.5 mm, QQ is a method for improving the spatial resolution of an axial image to, for example, about 0.35 mm. QQ will be described below.
[0016]
FIG. 35 shows that detectors that are composed of an even number of elements (channels) arranged in the channel direction are not mounted symmetrically with respect to the center line, and the detectors are mounted shifted by 1/4 channel in the channel direction. This is a state of so-called QQ offset attachment.
[0017]
At this time, in FIG. 36A, a path (thick arrow pointing upward) connecting the virtual k + 0.5 channel just in the middle of the kth channel and the k + 1th channel in the jth view and the focus of the jth view. 36B coincides with the path (thick arrow pointing downward) that connects the focus of the j + x view and the y-th channel, which are rotated about a half turn.
[0018]
Therefore, the data of the y-th channel in the j + x view of FIG. 36B is the data of the k + 0.5 channel in the j-th view of FIG. The relationship between j, k, x, and y described above is expressed as follows.
[0019]
[Expression 1]
y = Cent CH × 2- (k + 0.5)
x = {[(k + 0.5−Cent CH) × φ] / [Nch × 180] +0.5} × Nview
[∵ φ: Nch = ψ: (k + 0.5-Cent CH)
And (180 + 2ψ): x = 360: Nview] (see FIG. 37)
Nview = number of views per time, Nch = number of channels,
φ = Fan angle
ψ = angle to channel,
Cent CH = Center channel = (Nch + 0.5) / 2
(For QQ offset mounting, refer to FIG. 35)
[0020]
Therefore, virtual k + 0.5th channel data between the kth channel and the (k + 1) th channel is obtained from the data of the yth channel of the j + x view.
[0021]
However, when Nview = 1000, Nch = 1000, j = 100, k = 700, φ = 50 ° in the above formula, y = 300 channels, x = 555.625, and the 655.255 view. Will end up as data).
[0022]
Therefore, the integer part Ix = int (x) = 655 to interpolate the data D (655, 300) of the 300th channel in the 655th view and the data D (656, 300) of the 300th channel in the 656th view according to the following equation. As a result, data T · Data is obtained.
[0023]
[Expression 2]
T.Data = (1-w) .times.D (Ix, y) + w.times.D (Ix + 1, y)
Ix = int (x), w = x−Ix, D (j, k): k-th channel data in the j-th view
This data called the counter beam (see reference Bo in FIG. 36 (B) and FIG. 40 (C) described later) is the data of the (k + 0.5) th channel in the intended jth view.
[0024]
The virtual 0.5, 1.5, 2.5, 3.5, ..., k + 0.5, ..., 999.5 channel data (opposed beam) sandwiched between all detector elements in the jth view are the same. Get in the way.
The opposing beams of all channels are collectively referred to as opposing data. In almost all cases, x is a decimal, so that each counter beam is obtained by two data interpolation of 1 channel × 2 views.
This is repeated for all Nviews.
[0025]
The convolution processing (convolution) and backprojection processing described in (3) above are performed using the 2 × Nch channel data, which is obtained by the above-described method and has twice the number of conventional sampling points (double sampling density). (Back projection) and image reconstruction.
Since the opposite data is obtained by two-data interpolation as described above, the spatial resolution does not reach twice, but a spatial resolution of about 0.35 mm, which is 1.4 times, can be obtained.
[0026]
Here, the concept of QQ will be described again with reference to FIG.
Consider data of some jth view. The actual data collected in the jth view indicated by the solid line and the opposing data indicated by the dotted line obtained by interpolation of 1 channel × 2 views are alternately sandwiched, and the number of detection elements is doubled as indicated by reference numeral M1 in FIG. The image is reconstructed as data collected by a detector with high sampling density.
At this time, since the scanning method is conventional scanning, the slice position (sampling position in the Z-axis direction) of the actual data and the counter data is the same.
[0027]
(5) Helical scan image reconstruction
FIGS. 39A and 39B show the conventional scan and the helical scan, which are the two scan methods shown in FIGS. 32A and 32B, as viewed from the front side. The horizontal axis is the slice (Z-axis) direction, the vertical axis is the rotational phase (angle), and the sampling positions of each data are connected by arrows. Hereinafter, such a diagram is referred to as a scan diagram.
[0028]
In the conventional scan shown in FIG. 39A, 360 ° data necessary for the target slice plane corresponding to the step [1] is collected. As described above, [1] → [2] → Image reconstruction can be performed in step [3].
On the other hand, since the helical scan shown in FIG. 39B is a helical scan, only one view is collected on the target slice plane.
[0029]
Therefore, instead of the above [1], after obtaining the necessary data by interpolating the raw data obtained by correcting the collected projection data in the Z-axis direction, the above-mentioned filter-corrected back projection method [2] → [3] To reconstruct the image.
[0030]
Typical interpolation methods in single slice CT are the following two types.
[0031]
(a) 360 ° interpolation method
The 360 ° interpolation method will be described with reference to FIG.
As shown in FIG. 40A, this is a method of linearly interpolating the real data of the two closest views in the same phase across the target slice position with the inverse ratio of the distance between the slice plane and the sampling position.
[0032]
For example, if the target slice position (Z coordinate of the slice plane) is Z = Z0, the data collected at this slice position is only one view at a phase of 0 °. Therefore, for example, when obtaining data of the phase θ, the actual data 1 on the upper side of the slice position and the actual data 2 on the lower side are selected, and the distance between the Z coordinate obtained by sampling the respective actual data and the target slice position Z0. Linear interpolation is performed for each channel at an inverse ratio of (Z coordinate) to obtain interpolation data. This is repeated for all necessary phases.
[0033]
As shown in FIG. 41, FIG. 42 shows data of a certain j-th view.
[0034]
The first, second, third,..., Nch data in the actual data 1, and the first, second, third,..., Nch data in the actual data 2, respectively. Interpolated with the inverse ratio of the distances to obtain interpolated data.
[0035]
(b) Opposed beam interpolation method
This is a method of using opposite data that is virtual data.
As shown in FIG. 40C, the beam to each detection element of the actual data collected when the focal point is at the “black circle” position is as indicated by a solid line arrow. At this time, the left beam 1 and the dotted beam when the X-ray focal point is at the position of “white circle” are beams that pass through the same path. The beam from this “white circle” is referred to as a counter beam.
[0036]
Similarly, the dotted beam from beam 2 and light gray (coarse dots) and the beam from beam 3 and dark gray (dark dots) are opposite beams that pass through the same path. Thus, all the beams in the “black circle” have opposite beams.
[0037]
Therefore, virtual data (referred to as counter data) is formed by extracting the counter beam corresponding to each beam from the focus position data of white circle → light gray (coarse dot) → dark gray (dark dot). A method of performing linear interpolation between data and counter data is a counter beam interpolation method.
At this time, the counter beam is given by the following equation.
[0038]
[Equation 3]
y = Cent CH × 2-k
x = {[(k−Cent CH) × φ] / [Nch × 180] +1/2} Nview
[∵ φ: Nch = ψ: (k-Cent CH)
And (180 + 2ψ): x = 360: Nview] (see FIG. 37)
Nview = number of views per time,
Nch = number of channels,
φ = Fan angle
ψ = angle to channel
Cent CH = Center channel = (Nch + 0.5) / 2
(In the case of QQ offset installation)
[0039]
Accordingly, from the data of the y-th channel of the j + x view, virtual opposite data passing through the same path as the k-th channel with a shift of about a half rotation in the slice direction is obtained.
[0040]
The difference from the QQ reconstruction described above is that the virtual channel data of the path sandwiched between the actual data channels is obtained in QQ (see FIG. 36), but this time the same path as the actual data channel is obtained. Is obtained (see FIG. 43).
[0041]
However, when Nview = 1000, Nch = 1000, j = 100, k = 700, φ = 50 ° in the above formula, y = 300.5 channel, x = 555.4861, and the 655th. This is the data of channel 300.5 of 4861 views.
[0042]
Therefore, from the integer part Ix = int (x) = 655 and Iy = int (y) = 300, the data D (655,300) of the 300th channel and the data D (655,301) of the 301st channel in the 655th view The data (656, 300) of the 300th channel and the data D (656, 301) of the 301st channel in the 656th view are interpolated at four points according to the following formula to obtain the counter data T · Data. Each of the opposed beams is obtained by 4 data interpolation of 2 channels × 2 views.
[Expression 4]
T.Data = (1-w) .times. [D (Ix, Iy) + D (Ix, Iy + 1)] / 2 + w.times. [D (Ix + 1, Iy) + D (Ix + 1, Iy + 1)] / 2
Ix = int (x), w = x-Ix, Iy = int (y), D (j, k): data of the kth channel in the jth view
FIG. 41 is a conceptual diagram of counter beam interpolation of data of a certain jth view.
[0043]
The first, second, third,..., Nch data in the actual data, and the first, second, third,. Interpolated for each channel data at the inverse ratio of the position distance to obtain interpolated data.
[0044]
As described above, each beam of the opposing data is obtained from data of different views, but since the current scan method is a helical scan, the slice position differs for each view. Therefore, as shown in FIG. 41, the slice position of the counter beam differs for each channel.
[0045]
In this method, the 360 ° interpolation method interpolates between data at slice positions shifted by one rotation, whereas the opposite beam interpolation method has a difference of about half rotation between the slice position of the actual data and the opposite data. The counter beam interpolation method is superior in the resolution in the slice direction.
However, the spatial resolution in the axial plane is about 0.50 mm, which is about the same as the conventional scan in the 360 ° interpolation method, and is less than 0.50 mm because the opposite data is obtained by four-point interpolation in the opposite beam interpolation method. .
[0046]
(6) Multi-slice CT
Now, in response to a request to capture a wide range at high speed with high definition, as shown in FIGS. 44 (A), (B), and (C), a plurality of detector rows such as 2, 4, and 8 rows are arranged. A multi-slice CT system is proposed.
[0047]
Some terms will be described by taking the 4-row multi-slice CT of FIG. 44 (B) as an example.
[0048]
FIG. 34A is viewed from the Z-axis direction, and the circle in the figure is the effective visual field FOV as described above.
FIG. 34B is an observation of a plane including the Z-axis from a direction perpendicular to the Z-axis. X-rays incident on the detector element from the X-ray focal point pass through the rotation center (from the X-ray focal point). The thickness of the beam in the Z-axis direction (of distance FCD) is defined as a basic slice thickness T.
[0049]
(7) Known examples of helical scan in multi-slice CT
The helical scan in multi-slice CT is described in the following document 1.
[0050]
Japanese Patent Laid-Open No. 4-224363 “CT Apparatus”
Hiroshi Aratsuki, Junjiro Nanbu (filed on Dec. 25, 1990) (Reference 1)
The helical pitch P in the multi-slice CT extends the concept of the basic pitch in the single-slice CT described above, and is the product of the number of detector rows N and the basic slice thickness T, that is, the rotation center, as shown in the following equation (1). This is the same as the total slice thickness.
[0051]
P = N × T (1)
Hereinafter, the helical pitch is expressed by a value obtained by dividing the helical pitch by the basic slice thickness. In equation (1), a helical scan with a pitch of 4 is used.
One of the interpolation methods proposed in the literature 1 when helical scanning is performed with pitch N in N-row multi-slice CT is an extension of the 360 ° interpolation method of single-slice CT.
[0052]
FIG. 45 is a scan diagram showing the above method in 4-row multi-slice CT. Similar to the 360 ° interpolation method of FIG. 40A, this is an interpolation method using two actual data sandwiching the target slice position. This is tentatively referred to as an adjacent interpolation method, which is also described in the above-mentioned document 1.
[0053]
(8) Unreleased example of helical scan in multi-slice X-ray CT
Although it is not a well-known example, the following three types of methods are described in the following document 2.
[0054]
Japanese Patent Application No.7-337123 "X-ray CT system"
Katsuyuki Taguchi, Hiroshi Aratsuki (filed December 25, 1995) (Reference 2)
Although this is not a problem to be solved by the present invention, a part of this method is used when the present method is applied to multi-slice CT, so an outline of the contents will be described.
[0055]
(A) Means 1: High-density sampling / scanning method: Pitch = 2.5, 3.5, 4.5 for 4 rows, Pitch = 1.5 for 2 rows
First, there is a description about a helical pitch, and there is a description about a method for increasing the sampling density by a helical scan with a pitch of Pitch = 2.5, 3.5, 4.5 or the like.
[0056]
(B) Mean 2: New opposite beam interpolation method: Interpolation method between opposite beams
Second, there is a description of how to use the counter beam. There is a description regarding a combination of an interpolation method based on a combination of real data / real data and opposite data / opposite data and a helical pitch based on a normal helical pitch or a high-density sampling method.
[0057]
(C1) Mean 3: Filter interpolation method 1 (filter interpolation method using sampling data / filter processing)
(C2) Mean 4: filter interpolation method 2 (interpolation data / filter interpolation method using weighted addition (filter) processing)
(C3) Mean 5: Filter interpolation method 3 (Filter interpolation method by processing virtual scan raw data)
(C4) Mean 6: Filter interpolation method 4 (filter interpolation method by processing reconstructed voxel data)
The third is a description regarding the interpolation method. There are four methods for filtering in the slice direction, including a combination with a normal helical pitch or a helical pitch by a high-density sampling method, and a combination with a new counter beam interpolation method.
[0058]
[Problems to be solved by the invention]
However, although the above-mentioned QQ can obtain a resolution of 0.35 mm on the axial plane, since it is a conventional scan, the continuity in the slice direction is poor and it is not suitable for obtaining three-dimensional volume data.
On the other hand, image reconstruction with interpolation in helical scan has good continuity in the body axis direction and is suitable for obtaining three-dimensional volume data, and the opposed beam interpolation method can provide high spatial resolution in the body axis direction. The spatial resolution of the surface will be 0.50 mm or less.
[0059]
Accordingly, an object of the present invention is to provide an X-ray CT apparatus that satisfies all of high spatial resolution (for example, 0.35 mm) of an axial surface and good continuity in the body axis direction in helical scanning.
[0060]
[Means for Solving the Problems]
In order to solve the above-mentioned problems, an invention according to claim 1 is to detect an X-ray beam generation source and X-rays emitted from the X-ray beam generation source. A detector row in which a plurality of detector elements constituting a channel are arranged along the channel direction is arranged in a plurality of rows in the slice direction. A detection means provided; A rotating gantry holding the X-ray beam generation source and the detection means; Subject is placed A bed along the slice direction A moving means for moving the X-ray beam while rotating the X-ray beam generating source; bed In an X-ray CT apparatus that scans the subject spirally and collects data via the detection means, and reconstructs an image of a target slice position,
Corresponding to the virtual channel sandwiched between the real data collected by the detection means and the channel of the real data, and using the opposing data sandwiching the slice position, the data collected by the detection means Channel direction Data of a sampling pitch finer than the sampling pitch (fine pitch data) is generated, and image reconstruction is performed based on the fine pitch data. Three or more actual data and the opposite data in the slice direction are arranged in the slice direction. The fine pitch data is generated by filtering.
[0061]
According to the first aspect of the present invention, for example, as shown in FIG. 3, the fine pitch is finer than the data (actual data 1, 2) collected by the detection means (21, 23, etc. in FIG. 1) while performing helical scanning. Data SP is generated, and image reconstruction is performed based on the fine pitch data SP. In this way, both high spatial resolution (for example, 0.35 mm) of the axial surface as in QQ shown in FIG. 38 and good continuity in the body axis direction in the helical scan can be satisfied.
[0062]
According to a second aspect of the present invention, an X-ray beam generation source and X-rays emitted from the X-ray beam generation source are detected. A detector row in which a plurality of detector elements constituting a channel are arranged along the channel direction is arranged in a plurality of rows in the slice direction. A detection means provided; A rotating gantry holding the X-ray beam generation source and the detection means; Subject is placed A bed along the slice direction A moving means for moving the X-ray beam while rotating the X-ray beam generating source; bed To collect data via the detection means by scanning the subject in a spiral shape, The image of the target slice position In an X-ray CT apparatus for image reconstruction,
Corresponding to the virtual channel sandwiched between the real data collected by the detection means and the channel of the real data, and using the opposing data sandwiching the slice position, the data collected by the detection means Channel direction Generate data with more sampling points than the number of sampling points (multi-point data), perform image reconstruction based on the multi-point data, and filter the three or more actual data and the opposite data in the slice direction in the slice direction Generating the multi-point data.
[0063]
According to the second aspect of the present invention, sampling data (multi-point data) having a larger number of points than the number of points (eg, 1000 points) sampled by the detection means (21, 23, etc. in FIG. 1) while performing a helical scan on the subject. , For example, 2000 points), and image reconstruction is performed based on the multipoint data. In this way, both high spatial resolution (for example, 0.35 mm) of the axial surface as in QQ shown in FIG. 38 and good continuity in the body axis direction in the helical scan can be satisfied.
[0076]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, the present invention will be described based on the illustrated embodiment.
(I) First embodiment
FIG. 1 is a system configuration diagram of an apparatus to which this embodiment is applied, FIG. 2 is a detailed configuration diagram of the interpolation processing unit 29 in FIG. 1, and FIG. 3 is a conceptual diagram of processing of this embodiment. FIG. 4 is a flowchart showing the flow of processing of this embodiment.
[0077]
As shown in FIG. 1, an X-ray CT apparatus 10 includes a system control unit 11, a gantry / bed control unit 13, a bed moving unit 15, an X-ray control device 17, a high voltage generator 19, and an X-ray. A beam generation source 21, a detector 23, a rotating mount 25, a data collection unit 27, an interpolation processing unit 29, an image reconstruction unit 31, and a display unit 33 are provided.
[0078]
The system control unit 11 includes a gantry / bed control unit using a rotation speed, a slice thickness, a fan angle, etc. as a gantry / bed control signal among helical scan conditions such as slice thickness and rotation speed input using an input device (not shown). 13 is output.
[0079]
Further, the system control unit 11 outputs an X-ray beam generation control signal for controlling X-ray beam generation to the X-ray control device 17, and outputs a detection control signal indicating the detection timing of the X-ray beam to the data collection unit 27. Output for.
[0080]
Further, the system control unit 11 outputs a data collection control signal for data collection to the data collection unit 27 and outputs an interpolation control signal indicating an interpolation method to the interpolation processing unit 29.
The gantry / bed control unit 13 rotates the rotating gantry 25 based on the gantry / bed control signal output by the system control unit 11 and outputs a bed movement signal to the bed movement unit 15.
[0081]
The couch moving unit 15 obtains the amount of movement of the couch 15a per rotation of the rotating gantry 25 based on the couch movement signal output by the gantry / couch control unit 13, and moves the couch 15a by this moving amount.
[0082]
The X-ray controller 17 controls the timing of high voltage generation by the high voltage generator 19 based on the X-ray beam generation control signal output by the system controller 11.
[0083]
The high voltage generator 19 supplies a high voltage for exposing the X-ray beam from the X-ray beam generation source 21 to the X-ray beam generation source 21 in accordance with a control signal from the X-ray control unit 17.
The X-ray beam generation source 21 exposes the X-ray beam with a high voltage supplied from the high voltage generator 19.
[0084]
The detector 23 is a single slice detector that collects projection data transmitted through the subject.
The rotary mount 25 holds the X-ray beam generation source 21 and the detector 23. The rotating gantry 25 is rotated around a rotation axis passing through an intermediate point between the X-ray beam generation source 21 and the detector 23 by a gantry rotating mechanism (not shown).
[0085]
The data collection unit 27 collects the X-ray beam (actually a detection signal) detected by the detector 23 in correspondence with the data collection control signal output by the system control unit 11.
The interpolation processing unit 29 interpolates the X-ray beam at the target slice position based on the X-ray beam collected by the data collection unit 27. The interpolation processing unit 29 includes a CPU, a memory, and the like. The detailed configuration of the interpolation processing unit 29 is shown in FIG. 2 as described above.
[0086]
The image reconstruction unit 31 reconstructs an image based on the X-ray beam interpolated by the interpolation processing unit 29.
The display unit 33 displays the image reconstructed by the image reconstruction unit 31 on a monitor (not shown).
[0087]
Next, the operation of the present embodiment will be described by dividing it into (1) CT general operation and (2) interpolation processing.
(1) General operation
First, the operator inputs a helical scan condition using an input device (not shown). For example, the following helical scan conditions are used.
[0088]
Number of detector rows Nseg = 1
Number of detector channels Nch = 1000
Thickness at the center of rotation of the detector in the Z-axis direction Dseg = 20 mm
Beam thickness at the center of rotation Nseg × Dseg = 20mm
Distance between focus and rotation center FCD = 600mm
Focus-detector distance FDD = 1200mm
Effective viewing diameter FOV = 500mm
Effective viewing angle (fan angle) φ = 50 °
[0089]
When the helical scan condition is input, the system control unit 11 outputs the rotational speed, slice thickness, fan angle, etc., to the gantry / bed control unit 13 as a gantry / bed control signal. . The gantry / bed control unit 13 outputs a bed movement signal to the bed movement unit 15 based on the gantry / bed control signal.
[0090]
In this state, when a couch start command is input from the input device by the operator, the system control unit 11 instructs the gantry / couch control unit 13 to start diagnosis and controls X-ray beam generation. A beam generation control signal is output to the X-ray controller 17. In response to the X-ray beam generation control signal, the X-ray controller 17 generates a high voltage from the high voltage generator 19.
[0091]
As a result, the X-ray beam is exposed from the X-ray beam generation source 21, and the bed 15a is moved by the bed moving unit 15, and diagnosis by helical scanning is started.
When the data acquisition control signal is output from the system control unit 11, the data acquisition unit 27 detects an X-ray beam from the detector 23 in correspondence with the data acquisition control signal, and detects the detected X-ray beam ( Actually, the detection data) is supplied to the interpolation processing unit 29.
[0092]
When the X-ray beam is supplied, the interpolation processing unit 29 interpolates the X-ray beam at the target slice position based on the X-ray beam. This interpolation process will be described in the following (2).
[0093]
(2) Interpolation process
FIG. 3 is a conceptual diagram of the interpolation process, and FIG. 4 is a flowchart showing the flow of the interpolation process.
FIG. 3 shows a plurality of data of a certain phase θ. Consider the jth view as the lower real data 1 and the j + Nview view as the upper real data 2 across the slice position. These are the same as the data used in the above-described 360 ° interpolation method (see FIGS. 40A and 42).
[0094]
Further, consider the opposite data 1 on the lower side and the opposite data 2 on the upper side across the slice position.
[0095]
(1) First interpolation process
Interpolation means 1 (29C) obtains real data 1 and real data 2 from the raw data storage unit 29B, and performs linear interpolation with the inverse ratio of the distance between the slice position of the data and the target slice position, The interpolation data 1 at the target slice position is obtained and stored in the interpolation data 1 storage unit 29D (step S11 in FIG. 4). The interpolation weight is constant for all channels.
Interpolated data 1 is data of the first, second, third,..., 1000 channels (hereinafter also referred to as integer channels).
[0096]
(2) Opposite data generation
The opposing data generation unit 29E reads out necessary data from the raw data storage unit 29B, and interpolates the corresponding 1 channel × 2 view data by the method of QQ reconstruction described in the conventional example (see FIG. 38). Thus, opposite data of a virtual channel (hereinafter, this virtual channel is also referred to as a minority channel) sandwiched between real data channels is generated. At this time, the opposite data 1 on the lower slice position and the opposite data 2 on the upper slice position are generated (step S13 in FIG. 4).
[0097]
The generated counter data 1 and counter data 2 are data of the 0.5th, 1.5th, ..., 999.5th channels.
Since the generated opposite data 1 and opposite data 2 are not interpolated in the slice direction, FIG. 3 shows the slice position changed for each channel.
[0098]
(3) Second interpolation process
Interpolation means 2 (29F) linearly interpolates opposite data 1 and opposite data 2 with the inverse ratio of the distance between the slice position of both data and the target slice position, and obtains interpolation data 2 at the target slice position. Then, it is stored in the interpolation data 2 storage unit 29G (step S15 in FIG. 4). The interpolation weight is calculated for each channel.
Interpolation data 2 is data of the 0.5th, 1.5th, ..., 999.5th channels.
[0099]
(4) High density data generation processing
The high-density data generation means 29H reads the interpolation data 1 (integer channel data) from the interpolation data 1 storage unit 29D and the interpolation data 2 (minority channel data) from the interpolation data 2 storage unit 29G, respectively, and alternates each data. Interpolated data 3 having twice the sampling point is obtained (step S17 in FIG. 4). At this time, channel numbers from 1 to 2000 (= 2 × Nch) are newly assigned to the individual data constituting the interpolation data 3.
[0100]
(5) Filter corrected back projection
The image reconstruction unit 31 reconstructs an image by, for example, a normal filter correction back projection method (step S19 in FIG. 4).
[0101]
Since the obtained image is reconstructed using data of 2 × Nch sampling points, the spatial resolution of the axial plane is about 0.35 mm, which is the same as QQ processing, and uses helical scan data. Therefore, it is excellent in continuity in the body axis direction. Both advantages are compatible.
[0102]
In this embodiment, the example in which the interpolation data 2 is obtained by linear interpolation using the opposing data 1 on the lower slice position and the opposing data 2 on the upper slice position is described, but the present invention is not limited to this.
[0103]
For example, non-linear interpolation may be used, opposing data 1 below the slice position in the center channel and opposing data 2 above the slice position in the center channel may be selected, and extrapolation may be used depending on the channel as shown in FIG. .
[0104]
In FIG. 1, the subject may be small (for example, the head) as indicated by the two-dot chain line with respect to the maximum FOV. In such a case, as shown in the lower right of FIG. 5, the number of channels may be halved (1000 in this case) for interpolation processing. In this way, image reconstruction can be performed with a small memory size, and high-speed processing can be performed.
[0105]
Alternatively, as shown in FIG. 6, the selection of the opposing data may be changed for each channel so that interpolation interpolation is always performed.
Further, in this embodiment, the interpolation processing unit and the image reconstruction unit are configured separately, but a configuration in which the control device of the image reconstruction unit performs the interpolation processing as shown in FIG.
[0106]
(II) Second embodiment
This embodiment is a case of debluring processing in the channel direction.
The system configuration of the apparatus of this embodiment is the same as that of the first embodiment, and the detector that collects projection data of the subject is a single slice detector.
[0107]
FIG. 8 shows a detailed configuration of the interpolation processing unit 29 in the present embodiment. In addition to the configuration of the interpolation processing unit 29 in the first embodiment, a channel direction debluring means 29J and an interpolation data 4 storage unit 29K are provided.
Since the processes from (1) to (4) are the same as those in the first embodiment, description thereof will be omitted.
[0108]
(1) First interpolation process
(2) Opposite data generation
(3) Second interpolation process
(4) High density data generation processing
Interpolation data 3 which is high-density data is obtained by the above processes (1) to (4).
[0109]
(5) Channel direction debluring process
FIG. 9 is a conceptual diagram of channel direction debluring processing.
Here, the sampling interval and each sampling width of the interpolation data 1, the interpolation data 2, and the interpolation data 3 will be considered. For the sake of simplicity, the following description will be made assuming that the channels are arranged in a straight line and an X-ray beam is incident on each detection element in parallel.
[0110]
Interpolation data 1 and interpolation data 2 have a total number of sampling points of Nch, a sampling interval is a channel interval d, and a sampling width of each sampling point is also d. On the other hand, in the interpolation data 3, the total number of sampling points is 2Nch, the sampling interval is d / 2, and the sampling width of each sample point is d. That is, it can be seen that these data overlap each other and include redundancy.
[0111]
Therefore, in order to recover the redundancy due to the duplication, the interpolation data 3 is filtered by a blur recovery filter having an enhancement effect in the channel direction. This type of processing is called deconvolution processing or Debluring processing, and is well known to other companies in the same industry with QQ processing. An example of the blur recovery filter is described in the following documents 3 and 4. However, the present invention is not limited to this, and an appropriately deformed filter may be used.
[0112]
JP-A-61-74071 (filed on September 19, 1984) "X-ray CT apparatus"
Isao Horiba, Akira Iwata, Hiroshi Sasaki, Kazuhiro Sato (Reference 3)
JP 61-290573 (filed on June 19, 1984) "X-ray CT apparatus"
Hiroshi Nishimura (Reference 4)
The channel direction debluring means 29J reads the interpolation data 3, convolves the interpolation data 3 and the debluring filter DF • CH, obtains fourth interpolation data, and stores it in the interpolation data 4 storage unit 29K. The fourth interpolation data is data whose redundancy has been recovered.
[0113]
(6) Filter-corrected back projection
The image reconstruction unit 31 reconstructs an image by a normal filtered back projection method. Since the reconstructed image uses the fourth interpolation data whose redundancy has been recovered, the image has a higher spatial resolution.
[0114]
In the above description, the debluring processing in the channel direction and the convolution of the reconstruction filter of the filter-corrected back projection method are processed separately, but they may be performed simultaneously. Since the convolution process is a linear process, the filter F1 and the filter F2 are convolved sequentially with respect to the data D, and the filter F3 obtained by convolving the filter F1 and the filter 1F2 is convolved with the data D once. Is mathematically equivalent.
[0115]
[Equation 5]
Data = F2 * (F1 * D) = (F2 * F1) * D = F3 * D,
F3 = F2 * F1
Therefore, if the data is processed using a filter obtained by convolving the filter used in the debluring process and the reconstructed filter of the filter-corrected back projection method in advance, it is efficient because only one convolution process is required.
[0116]
In the above description, the debluring process for the interpolation data 3 has been described, but the interpolation data 2 is also an interpolation of 1 channel × 2 view data at the time of generation of the opposing data, and includes blur due to interpolation. Therefore, in addition to the above-described debluring processing, the interpolation data 2 may be subjected to debluring processing to recover this blur. This may be processed for the opposing data 1 and the opposing data 2, or may be processed for the interpolation data 2 obtained by interpolating the opposing data 1 and the opposing data 2.
[0117]
(III) Third embodiment
This embodiment is a case of multi-slice CT.
The system configuration of the apparatus of the present embodiment is the same as that of the first embodiment. However, a multi-slice CT system having a 4-row multi-slice detector is used (see FIG. 44B).
[0118]
The detailed configuration of the interpolation processing unit 29 in the present embodiment is the same as that in the first embodiment. That is, the operation is different, but the configuration is the same.
[0119]
When a helical scan is performed with Pitch = 4 in a 4-row multi-slice CT, the opposite beam of a certain row has a sampling position of actual data in a different row in the center channel as shown in FIG. This is a series of data that are almost identical (refer to Reference 2 for details). Since the sampling positions of the real data and the counter data are close to each other, this is a great disadvantage when interpolating between the real data and the counter data, but this is actively used in the third embodiment.
[0120]
FIG. 11 shows a state of a certain phase θ acquired by the four-row multi-slice CT.
The actual data for the four columns of the jth view, the opposite data for the four columns generated from the data of the j + x view as in the first embodiment, and the j + Nview view after one rotation (Nview view) Of the four columns of data, two opposing data generated from the first and second data of j + x + Nview, and the j + x before one rotation. − A total of eight data of two opposing data generated from the data in the third and fourth columns of the Nview view are shown. Originally, there are only the following numbers of data, but only a part is shown here.
[0121]
[Formula 6]
(Number of data) = (Number of rotations of helical scan) × (Number of detector rows) × 2
FIG. 12 is a conceptual diagram of processing in the third embodiment.
Although details will be described below, (2), (4), (5), and (6) are the same as those in the first embodiment, and the description thereof will be omitted. The difference from the first embodiment is that the interpolation means 29C and the counter data generation means 29E are added with functions as data selection means.
[0122]
(1) Selection of actual data 1 and actual data 2 (view and column)
The interpolation means 1 (29C) selects the two actual data that are closest to each other across the slice position from the actual data of the plurality of views and the plurality of detector arrays as shown in FIG. And
[0123]
(2) First interpolation process
(3) Generation of opposite data 1 and opposite data 2 (independent for each channel)
The opposing data generation means 29E selects the two closest opposing beams for each channel across the slice position so that interpolation interpolation is always performed in the second interpolation processing. In the helical scan of Pitch = 4 in 4-row multi-slice CT, the shift of the slice position for each channel is large, so it is better to select the optimum data independently for each channel than to generate the opposing data continuous over all channels. Good.
[0124]
At this time, as in (1), it is necessary to select which detector row of which view is to be used. Depending on the selected result, the raw data necessary for generating the counter data is read from the raw data storage unit 29B.
Similarly to the first embodiment, the read data is subjected to interpolation processing of 1 channel × 2 views to generate opposing data 1 and opposing data 2.
[0125]
In FIG. 11, a large amount of facing data is displayed for the sake of explanation. However, it is more efficient to generate all of these data after selection rather than selecting them after generation. The opposite data 1 on the lower side of the slice and the opposite data 2 on the upper side of the slice shown in FIG. 12 are generated.
[0126]
(4) Second interpolation process
(5) High density data generation processing
(6) Filter-corrected back projection
Since the slice positions in the center channel of the actual data 1 and the opposite data 1 and the actual data 2 and the opposite data 2 are the same, the interpolation data 1 and the interpolation data 2 obtained in (2) and (4) are single slices. It is less susceptible to changes in the subject in the slice direction than CT. Therefore, an image reconstructed from the high-density data generated this time from such interpolation data 1 and interpolation data 2 has good image quality, and the spatial resolution in the slice direction is also high.
[0127]
In this embodiment, the example of Pitch = 4 in the 4-row multi-slice CT has been described. However, the present embodiment is not limited to this. Pitch = 2 may be set in 4 columns, or Pitch = even in other numbers such as 2, 3, 5, 6, 7, 8,. That is, the relationship between the number of columns and the pitch is determined so that the slice positions of the actual data and the counter data are substantially equal in the vicinity of the center channel.
[0128]
Further, regarding the selection of the opposing data, there are various modifications such as selection to use extrapolation as in the first embodiment.
Further, although the debluring processing in the channel direction described in the second embodiment is omitted, it may be combined with this.
[0129]
(IV) Fourth embodiment
This embodiment is a case of slice direction filter processing (bundling processing and debluring processing with a reduced basic slice thickness) in multi-channel CT.
FIG. 13 is a block diagram of the interpolation processing unit 29 in the present embodiment. The interpolation means 1 and the interpolation means 2 in FIG. 2 are replaced with the filter means 1 and the filter means 2, respectively.
[0130]
Further, the basic slice thickness T is smaller than the slice thickness T of the single slice CT, for example, T / 3. FIG. 14 shows a conceptual diagram of the scan state and processing in the phase θ.
Compared with FIG. 11 at the basic slice thickness T, it can be seen that the sampling density in the slice direction is increased in FIG.
[0131]
Further, it can be seen that since the sampling width of the original data in the slice direction, that is, the slice thickness is thin, good data in which the so-called partial volume effect is suppressed can be obtained.
[0132]
This time, three or more pieces of data are selected from this data group, and filter processing is performed independently for each channel to bundle or enhance them in the slice direction. Details of the filter processing are given in the above-mentioned document 2 (Japanese Patent Application No. 7-337123 “X-ray CT apparatus”). FIG. 15 shows a conceptual diagram of the filter processing combined with high-density sampling.
[0133]
Some examples of the shape of the filter function that performs the bundling process or the enhancement process are shown in FIGS. This time, it is assumed that a lot of data is bundled using the low resolution function of FIG.
Processes {circle around (5)} and {circle around (6)} are the same as those in the first embodiment, and the description thereof is omitted.
[0134]
(1) Actual data selection
The filter means 1 (29L) selects a plurality of real data of slice positions within a range necessary for the filtering process from the real data of a plurality of views and a plurality of detector arrays as shown in FIG. Read from the raw data storage unit 29B.
[0135]
(2) Filter processing 1
The filter means 1 (29L) filters the actual data group selected in (1) in the slice direction independently for each channel with the filter shown in FIG. And stored in the interpolation data 1 storage unit 29D.
[0136]
The filtering method may be a method of directly processing the actual data group described in the third embodiment example of the document 2, or the real data group described in the fourth embodiment example of the document 2 may be resampled. A method of processing the data group obtained in this way may be used.
[0137]
(3) Opposite data generation
As shown in FIG. 14, the counter data generation unit 29E is a counter data of slice positions within a range necessary for the filtering process from among a plurality of counter data that can be generated by real data data of a plurality of views and a plurality of detector arrays. Are selected (referred to as opposed data group), and necessary data is read from the raw data storage unit 29B.
The 1-channel × 2-view interpolation process is performed on the read data in the same manner as in the first embodiment to generate a counter data group.
[0138]
(4) Filter means 2
The filter means 2 (29M) filters the opposite data group generated by the opposite data generation means 29E in the slice direction independently for each channel with the filter shown in FIG. And stored in the interpolation data 2 storage unit 29G. The filter processing method conforms to (2).
[0139]
(5) High density data generation processing
(6) Filter-corrected back projection
Originally using data with a partial effect suppressed, high-density data is generated from interpolation data 1 and interpolation data 2 that are added in the slice direction by a low resolution filter function, and is a reconstructed image. The partial effect of the image is extremely suppressed.
[0140]
In this embodiment, the filter processing using a filter that is bundled in the slice direction has been described. However, the present invention is not limited to this. You may perform what is called Debluring processing using the filter which improves the spatial resolution of a slice direction. Since a sufficient sampling density is originally obtained in the slice direction, the effect of the debluring process is great. Further, in combination with the high density sampling described in the fifth embodiment described below, the image quality is further improved in this case.
[0141]
Further, the detector need not be a multi-slice detector, and may be a single slice. Further, the conventional basic slice thickness may be used without reducing the basic slice thickness.
Further, when combined with the debluring process described in the second embodiment, the spatial resolution of the axial plane is further improved. Also in the filter processing in the slice direction, if a filter having a debluring effect as described above is used, high spatial resolution can be obtained in both the axial plane and the slice direction.
[0142]
(V) Fifth embodiment
This embodiment is a case of helical pitch 2.5 in multi-channel CT.
The configuration of the interpolation processing unit 29 in the present embodiment is the same as that in the fourth embodiment.
[0143]
This time, helical scanning is performed by a high-density sampling / scanning method in which the helical pitch is Pitch = 2.5, and the basic slice thickness is T.
FIG. 17 shows a scan diagram of only the actual data in the high-density sampling / scanning method with Pitch = 2.5, and FIG. 18 shows a scan diagram of the actual data and the opposite data (in the center channel). It can be seen that the sampling density in the slice direction is increased in the high-density sampling / scanning method.
[0144]
FIG. 19 shows a scan state at the phase θ. Compared to FIG. 11 showing the scan state when Pitch = 4, it can be seen that the sampling density in the slice direction is increased. For the filtering process in the slice direction, the standard function of FIG.
[0145]
Processes (2), (4), (5), and (6) are the same as those in the fourth embodiment, and thus description thereof is omitted. The filter processing after data selection is the same as that in FIG. 14 of the fourth embodiment, and is therefore omitted.
[0146]
(1) Actual data selection
The filter means 1 (29L) selects a plurality of actual data of slice positions within a range necessary for the filtering process from a plurality of views and a plurality of detector arrays arranged in a complicated order as shown in FIG. (Referred to as a real data group) and read from the raw data storage unit 29B.
[0147]
(2) Filter processing 1
(3) Opposite data generation
The counter data generation unit 29E includes a plurality of counter data arranged in a complex order that can be generated by data of actual data of a plurality of views and a plurality of detector arrays arranged in a complex order as shown in FIG. Then, a plurality of opposing data at slice positions in a range necessary for the filter processing are selected (referred to as an opposing data group), and necessary data is read from the raw data storage unit 29B.
The read data is subjected to interpolation processing of 1 channel × 2 views in the same manner as in the first embodiment to generate a counter data group.
[0148]
(4) Filter processing 2
(5) High density data generation processing
(6) Filter-corrected back projection method
Since the data obtained by high-density sampling in the slice direction is filtered while the basic slice thickness is T, a high-quality image is obtained.
[0149]
In this embodiment, the example of Pitch = 2.5 in the 4-row multi-slice CT has been described. However, the present embodiment is not limited to this. Pitch = 1.5, 2.0, 3.0, 3.5, 4.5, or Pitch = 1.5 in a two-row multi-slice can be arbitrarily modified.
[0150]
As an example, FIGS. 20 to 22 and FIGS. 23 to 26 show scan diagrams when Pitch = 3.5 in four rows, Pitch = 4.5, and Pitch = 1.5 in two rows.
[0151]
Further, it may be combined with the debluring process described in the second embodiment or the fourth embodiment.
[0152]
(VI) Sixth embodiment
In the first to fifth embodiments described above and these modifications, the first interpolation data, which is integer channel data at the target slice position, is obtained by subjecting actual data to interpolation processing and / or filtering processing in advance. Similarly, the opposite data is interpolated and / or filtered to obtain second interpolation data which is the minority channel data of the target slice position, and the target slice position is obtained by combining these first and second interpolation data. Of high density data.
[0153]
However, the present invention performs interpolation processing in the channel direction in advance to generate high-density real data that is first interpolation data and high-density counter data that is second interpolation data, and these first and second interpolation data. Based on the above, it is also possible to generate high-density data of the target slice position by the opposed beam interpolation process (helical interpolation process).
[0154]
In the sixth embodiment, after high-density real data and opposite data are generated by interpolation, high-density data at a target slice position is obtained by helical interpolation using these data. Is.
[0155]
The system configuration of the X-ray CT apparatus to which the sixth embodiment is applied is the same as that of the X-ray CT apparatus shown in FIG. 1, but the detailed configuration of the interpolation processing unit 29 is different.
[0156]
FIG. 27 is a detailed configuration diagram of the interpolation processing unit 29 in FIG. 1, FIG. 28 is a conceptual diagram of the processing of this embodiment example, and FIG. 29 is a flowchart showing the processing flow of this embodiment example. .
[0157]
In FIG. 27, the interpolation processing unit 29 of the sixth embodiment includes an interpolation processing control unit 29A that controls the entire interpolation processing unit, and a raw data storage unit 29B that stores raw data obtained by scanning the subject. The high-density actual data generating means 29Q for generating high-density real data, the high-density opposing data generating means 29P for generating high-density opposing data, the helical interpolation means 29R, the interpolation data 1 storage unit 29D, and the interpolation data 2 A storage unit 29G and an interpolation data 3 storage unit 29I are provided.
[0158]
Since the raw data collected by scanning the subject is stored in the raw data storage unit 29B, it is the same as in the first embodiment, and the subsequent processing will be described.
[0159]
FIG. 28 shows a plurality of data of a certain phase θ. Consider the jth view as the lower real data 1 and the j + Nview view as the upper real data 2 across the slice position. These are the same as the data used in the above-described 360 ° interpolation method (see FIGS. 40A and 42).
Further, consider the opposite data 1 on the lower side and the opposite data 2 on the upper side across the slice position.
[0160]
(1) First high-density data generation process (actual data)
The high-density actual data generating unit 29Q obtains the actual data 1 and the actual data 2 from the raw data storage unit 29B, and performs interpolation between adjacent channels of the same view according to the following equation for each actual data, One high-density data group hp1 is generated and stored in the interpolation data 1 storage unit 29D (step S21 in FIG. 29). The interpolation weight is constant for all channels.
[0161]
If the actual data of the k-th channel in the j-th view is D (j, k),
[Expression 7]
hp1 (j, 2 × k) = D (j, k)
hp1 (j, 2 × k−1) = [D (j, k−1) + D (j, k)] / 2
Interpolation data 1 is data for the first, second, third,..., 2000 channels.
[0162]
(2) Second high-density data generation processing (opposite data)
The high-density counter data generation unit 29P reads out necessary data from the raw data storage unit 29B, and for the odd-numbered channel data, the QQ reconfiguration method described in the conventional example (see FIG. 38) 1 channel x 2 view data is interpolated, and for even channel data, 2 channel data collected in 2 views is used as in the data generated by the counter beam interpolation method described in the conventional example. The second high-density data hp2 is generated by four-point interpolation and stored in the interpolation data 2 storage unit 29G (step S23 in FIG. 29).
[0163]
(2-1) Odd channel
[Equation 8]

(2-2) Even channel
[Equation 9]

[0164]
Alternatively, the odd-numbered channel data obtained in (2-1) may be interpolated as shown in (1) for even-numbered channel data. Further, the generation order of the first and second high-density data is reversed, the high-density opposed data that is the second high-density data is generated first, and the high-density real data that is the first high-density data is generated later. May be.
[0165]
(3) Helical interpolation processing
The helical interpolation means 29R reads the first high-density data (actual data) and the second high-density data (opposite data) from the interpolation data 1 storage unit 29D and the interpolation data 2 storage unit 29G, respectively, and performs helical interpolation in the slice direction. Then, the data of the target slice position is created and stored in the interpolation data 3 storage unit 29I (step S25). The interpolation may be two-point interpolation as in the first embodiment, or may be filter processing in the slice direction as in the fourth embodiment. Further, blur recovery processing (debluring processing) in the channel direction or slice direction may be performed.
[0166]
(4) Fan beam reconstruction processing
The image reconstruction unit 31 performs normal fan beam reconstruction using the high-density data stored in the interpolation data 3 storage unit 29I to obtain an image (step S19). The reconstruction method to be applied may be the filtered back projection method used in the first embodiment, or may be processed by combining fan-para transformation and Fourier inverse transformation.
[0167]
FIG. 30 is a flowchart showing a modification of the sixth embodiment. In the sixth embodiment, the first and second high-density data generation processes are performed from the raw data. However, the second high-density data can be generated using the generation result of the first high-density data.
[0168]
(1) First high-density data generation process (actual data)
The high-density actual data generating unit 29Q obtains the actual data 1 and the actual data 2 from the raw data storage unit 29B, and performs interpolation between adjacent channels of the same view according to the following equation for each actual data, One high-density data group hp3 is generated and stored in the interpolation data 1 storage unit 29D (step S31 in FIG. 30). The interpolation weight is constant for all channels.
[0169]
If the actual data of the k-th channel in the j-th view is D (j, k),
[Expression 10]
hp3 (j, 1) = D (j, 1)
hp3 (j, 2 × k) = D (j, k)
hp3 (j, 2 × k + 1) = [D (j, k) + D (j, k + 1)] / 2
Interpolation data 1 is data for the first, second, third,..., 2000 channels.
[0170]
(2) Second high-density data generation processing (opposite data)
The high-density opposed data generation unit 29P reads necessary data from the interpolation data 1 storage unit 29D, and for the odd-numbered channel data, the QQ reconstruction method described in the conventional example (see FIG. 38) The corresponding 1 channel x 2 view data is interpolated, and for the even channel data, the 2 channel data collected in 2 views is used as in the data generated by the counter beam interpolation method described in the conventional example. The second high-density data hp4 is generated by the four-point interpolation, and is stored in the interpolation data 2 storage unit 29G (step S33 in FIG. 30).
[0171]
This interpolation formula is shown below.
[0172]
[Expression 11]
For 1 ≦ K ≦ 2 × Nch (= 2000),
hp4 (j, K) = hp3 (j + X (K), Y (K))
Y (K) = 2 × Nch−K + 1
X (K) = {[((K-Cent CH) × φ] / [Nch × 180] +0.5} × Nview
Cent CH = (2 × Nch + 1) / 2
The helical interpolation process (step S25 in FIG. 30) and the image reconstruction process (step S19 in FIG. 30) are performed in the sixth embodiment using the first and second high-density data hp3 and hp4 thus obtained. Since it is the same as that of a form example, the following description is abbreviate | omitted.
[0173]
The sixth embodiment described above and the modifications thereof can be arbitrarily combined with the second, third, fourth, and fifth embodiments. In other words, it can be applied to combination with channel direction debluring processing, application to a multi-slice CT apparatus, combination with slice direction filtering, helical scanning by a high-density sampling / scanning method in a multi-slice CT apparatus, etc. .
[0174]
In the case where the present invention is applied to a multi-slice CT apparatus, the number of detector rows is described as four in the embodiment, but the number of detector rows is not limited to four, but may be any number, such as 2, 3, 5, 6, 7, 8 It is apparent that the present invention can be applied to a multi-slice CT apparatus having a number of detector rows.
[0175]
Also, the image reconstruction method is not limited to the filter-corrected back projection method (convolution method), but back projection operation using fan-para conversion, back projection operation using fast Fourier transform (FFT), Fourier transform, and inverse Fourier. It is clear that various image reconstruction methods such as an image reconstruction method using, and an image reconstruction by linogram can be used.
[0176]
Further, in the embodiment, high-density data having twice the density is generated from the raw data, but the image reconstruction is performed by generating high-density data having a density of 3 times, 4 times, etc. It is also within the scope of the present invention. For example, when generating high density data with a triple density, the numbers after the decimal point of the minority channels are 0.33 and 0.67, and when generating high density data with a quadruple density, the decimal point of the decimal channel is used. The following numbers may be set to 0.25, 0.5, and 0.75.
[0177]
【The invention's effect】
As described above, according to the present invention described in claim 1, since data having a sampling pitch (fine pitch data) finer than the sampling pitch of the data collected by the detecting means is generated and reconstructed, high spatial resolution is achieved. An image of the axial plane can be obtained. By reducing the basic slice thickness and filtering in the slice direction, the partial volume effect is further suppressed, and a high-quality image with few artifacts can be obtained. Further, if the filter selected in the filter processing in the slice direction is a low resolution function, a high-quality image such as a stack processed image can be obtained.
According to the second aspect of the present invention, since data having a larger number of sampling points (multi-point data) than the number of sampling points of the data collected by the detecting means is generated and reconstructed, an image of an axial surface with high spatial resolution can be obtained. Obtainable. By reducing the basic slice thickness and filtering in the slice direction, the partial volume effect is further suppressed, and a high-quality image with few artifacts can be obtained. Further, if the filter selected in the filter processing in the slice direction is a low resolution function, a high-quality image such as a stack processed image can be obtained.
[Brief description of the drawings]
FIG. 1 is a system configuration diagram of an X-ray CT apparatus to which each embodiment of the present invention is applied.
FIG. 2 is a block diagram of an interpolation processing unit in the first embodiment.
FIG. 3 is a conceptual diagram of processing in the first embodiment.
FIG. 4 is a flowchart for explaining the flow of processing in the first embodiment.
FIG. 5 is a conceptual diagram when extrapolation is used as processing in the first embodiment.
FIG. 6 is a conceptual diagram when interpolation is used as processing in the first embodiment.
FIG. 7 is a block diagram in a case where the control device of the image reconstruction unit performs interpolation processing in the first embodiment.
FIG. 8 is a block diagram of an interpolation processing unit in the second embodiment.
FIG. 9 is a conceptual diagram of channel direction debluring processing;
FIG. 10 is a diagram showing an opposing beam in a certain row when a helical scan is performed with 4-row multi-slice CT and Pitch = 4.
FIG. 11 is a diagram showing an opposite beam in a certain row when a helical scan is performed with a four-row multi-slice CT and Pitch = 4 in the upper right diagram.
FIG. 12 is a conceptual diagram of processing of the third embodiment.
FIG. 13 is a block diagram of an interpolation processing unit in the fourth embodiment.
FIG. 14 is a conceptual diagram of a phase θ scan state and processing in the fourth embodiment.
FIG. 15 is a conceptual diagram of filter processing combined with high-density sampling.
FIG. 16 is a diagram illustrating an example of the shape of a filter function that performs bundling processing or enhancement processing;
FIG. 17 is a scan diagram of only actual data in the high-density sampling / scanning method with Pitch = 2.5.
FIG. 18 is a scan diagram of actual data and counter data (in the central channel) in the high-density sampling / scanning method with Pitch = 2.5.
FIG. 19 is a diagram illustrating actual data of a plurality of views and a plurality of detector arrays arranged in a complicated order.
FIG. 20 is a scan diagram of Pitch = 3.5 in 4-row multi-slice CT.
FIG. 21 is a scan diagram of Pitch = 4.5 in 4-row multi-slice CT.
FIG. 22 is a scan diagram of Pitch = 4.5 with the basic slice thickness being halved.
FIG. 23 is a scan diagram when Pitch = 1.5 in the two-row multi-slice CT.
FIG. 24 is a diagram illustrating a counter beam interpolation method in the case of a pitch of 1.5 in a two-row multi-slice CT.
FIG. 25 is a diagram showing a counter beam interpolation method in the case of a pitch of 3.5 in the 4-row multi-slice CT.
FIG. 26 is a diagram showing an opposing beam interpolation method in the case of a pitch of 4.5 in 4-row multi-slice CT.
FIG. 27 is a block diagram of an interpolation processing unit in the sixth embodiment of the X-ray CT apparatus according to the present invention.
FIG. 28 is a conceptual diagram of processing in the sixth embodiment of the X-ray CT apparatus according to the present invention.
FIG. 29 is a flowchart showing the processing procedure in the sixth embodiment.
FIG. 30 is a flowchart showing a processing procedure in a modification of the sixth embodiment.
FIG. 31 is a schematic configuration diagram of a single slice CT.
FIG. 32 is a conceptual diagram of a conventional scan and a helical scan.
FIG. 33 is a diagram for explaining image reconstruction of the X-ray CT apparatus;
FIG. 34 is a diagram illustrating the geometry of a multi-slice X-ray CT apparatus.
FIG. 35 is a diagram for explaining a state of QQ offset attachment.
FIG. 36 is a diagram for explaining QQ.
FIG. 37 is a diagram illustrating QQ.
FIG. 38 is a diagram for explaining the concept of QQ.
FIG. 39 is a scan diagram in a conventional scan and a helical scan.
FIG. 40A is a diagram illustrating the 360 ° interpolation method, FIG. 40B is a diagram illustrating the counter beam interpolation method, FIG. 40C is a diagram illustrating the counter beam, and FIG. 40D is a diagram illustrating the sampling position of the counter beam. It is.
FIG. 41 is a conceptual diagram of opposed beam interpolation of data of a certain j-th view.
FIG. 42 is a conceptual diagram of a 360 ° interpolation method.
FIG. 43 is a diagram illustrating counter beam interpolation in helical scan.
FIG. 44 is a conceptual diagram of multi-slice CT.
FIG. 45 is a scan diagram when the 360 ° interpolation method is applied to the 4-row multi-slice CT.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 13 ... Stand bed control part, 15 ... Bed moving part, 23 ... Detector, 27 ... Data collection part, 29 ... Interpolation processing part, 31 ... Image reconstruction part

Claims (2)

An X-ray beam generation source and X-rays emitted from the X-ray beam generation source are detected , and a plurality of detector rows in which a plurality of detection elements constituting a channel are arranged in the channel direction are arranged in the slice direction. A detection means provided in a row, a rotating gantry holding the X-ray beam generation source and the detection means, a moving means for moving a bed on which a subject is placed along the slice direction, and the X-ray beam An X-ray beam is generated while rotating the generation source, the bed is moved by the moving means, the subject is scanned spirally, data is collected through the detecting means, and an image of a target slice position is obtained. In an X-ray CT apparatus for reconstructing an image,
Sampling in the channel direction of the data collected by the detection means, using the opposing data corresponding to the virtual channel sandwiched between the real data collected by the detection means and the channel of the real data and sandwiching the slice position Generates sampling pitch data (fine pitch data) finer than the pitch, and performs image reconstruction based on the fine pitch data, and filters three or more actual data and opposite data in the slice direction in the slice direction An X-ray CT apparatus characterized by processing to generate the fine pitch data. An X-ray beam generation source and X-rays emitted from the X-ray beam generation source are detected , and a plurality of detector rows in which a plurality of detection elements constituting a channel are arranged in the channel direction are arranged in the slice direction. A detection means provided in a row, a rotating gantry holding the X-ray beam generation source and the detection means, a moving means for moving a bed on which a subject is placed along the slice direction, and the X-ray beam An X-ray beam is generated while rotating the generation source, the bed is moved by the moving means, the subject is scanned spirally, data is collected through the detecting means, and an image of a target slice position is obtained. In an X-ray CT apparatus for reconstructing an image,
Sampling in the channel direction of the data collected by the detection means, using the opposing data corresponding to the virtual channel sandwiched between the real data collected by the detection means and the channel of the real data and sandwiching the slice position Generate data with more sampling points than the number of points (multi-point data), perform image reconstruction based on the multi-point data, and filter the three or more actual data and the opposite data in the slice direction in the slice direction An X-ray CT apparatus for generating the multipoint data.

Images